1. Field of the Invention
This invention relates to communication systems and in particular to equalization of input signals representing information transmitted over transmission lines or from storage systems.
2. Related Art
Many digital data communications systems employ adaptive equalization to compensate for the distortion effects of changing channel conditions and disturbances on the signal transmission channel. The equalization process estimates the transfer function of the transmission channel and applies the inverse of the transfer function to the received signal so as to reduce or eliminate the distortion effects. Channel equalization typically employs filters that remove amplitude and phase distortions resulting from a frequency dependent time variant response of the transmission channel, for example, to thereby provide improved symbol decision capability. Equalization removes baseband intersymbol interference (ISI) caused by transmission channel disturbances including the low pass filtering effect of the transmission channel. ISI causes the value of a given symbol to be distorted by the values of preceding and following symbols, and essentially represents symbol “ghosts” since ISI includes advanced and delayed symbols with respect to a reference symbol location in a given decision region.
An adaptive equalizer is essentially a digital filter with an adaptive response to compensate for channel distortions. Several well-known algorithms are available for adapting the filter coefficients and thereby the filter response to converge the equalizer.
Significant effort has been spent enhancing adaptation algorithms for use in data transmission whether over communication systems or from storage mediums. Such adaptation algorithms are required to compensate for distortions introduced into the signals by the transmission medium through which the signals have traveled. Such transmission mediums might comprise, for example, optical networks, wireless networks, standard public switch telephone networks or even storage media where the signals have been stored and transmitted through interfaces to a user. In all of these situations, the media through which the signal is transmitted or in which the media is stored may, in fact, affect the contents of the signal. Accordingly, equalization networks are generally employed to place the signal as nearly as possible in its original form. Since however, the exact nature of this distortion as to which the signal is subjected at the receiving end may not be known at the receiving end, the structure and methods employed to equalize the received signals necessarily involve certain assumptions. Typically, the techniques for enhancing the adaptation algorithms focus on the value of the signal at the time the signal is sampled (known as the convergence time). Very limited effort has been spent with respect to continuous time adaptation for continuous time delay line equalizers. Similarly, little effort has been spent using forward error correction (“FEC”) statistics for carrying out adaptation of equalizer.
One technique which has been used to adapt the feedforward equalizer to compensate for signal distortion due to the transmission or storage media is known as discrete time least mean square based adaptation. Typically, the discrete time least mean square (“LMS”) based adaptation is applied to the feedforward equalizer followed by decision feedback. This technique, which has been widely explored as a starting point for various enhancements for discrete time adaptation can be expressed as:ci[k+1]=ci[k]+μ*si[k]*e[k]where:                k=denotes discrete time points;        μ=is an adaptation parameter;        ci=denotes the ith tap either feed forward or feedback;        si[.]=denotes the sampled input signal appropriately time aligned as applied to the ith tap; and            e[.]=denotes the sampled slicer error signal, computed as the difference between the slicer input and slicer output signal appropriately time aligned.
The continuous-time analog of the above adaptation is:             c      i        ⁡          (      t      )        =            ∫      0      t        ⁢          μ      ·              e        ⁡                  (          τ          )                    ·                        s          i                ⁡                  (          τ          )                    ·              ⅆ        τ            
One problem with this form of the adaptation is “coefficient drift” with fractionally spaced equalizers.
Forward error correction statistics have also been used for adaptation of equalizers to recover as much of the original signal as possible and to reduce the bit error rate (“BER”). A simple approach counts the total number of errors of the type 0 mapped to 1 and the total number of errors of the type 1 mapped to 0 and uses this to adapt a symbol-spaced linear equalizer with three taps. The symbol which is referred to is the width of a pulse representing either a 1 or a 0. This technique achieves some improvement in the BER. However, additional improvement is desirable.